Using Motivation Assessment as a Teaching Tool for Large Undergraduate Courses: Reflections From the Teaching Team
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Introduction: Student motivation is a critical predictor of academic achievement, engagement, and success in higher education. Motivating students is a crucial aspect of effective teaching. Statement of the Problem: Although there is a wealth of research on student motivation, practical guidance for putting theory into practice in challenging teaching environments (i.e., large-format introductory courses) is lacking. We discuss a first step toward motivating students: understanding how motivated they are and using that information to inform teaching. Literature Review: Anxiety, impeded motivation, and high student-to-teacher ratio are all challenges associated with teaching foundational introductory courses, such as statistics. The Expectancy-Value-Cost model of motivation provides theoretical background to assist with these courses. We discuss the implementation and use of motivation assessments as a teaching tool. Teaching Implications: Motivation assessments are feasible and useful while teaching large-format introductory courses. Instructor reflections lend insights as to how to use these assessments to improve pedagogy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.010 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it